An Evolutionary Computation Model of Intracellular Signaling Networks

We use evolutionary computation (EC) methods to simulate the evolution of a particular class of intracellular signaling networks. This class of signaling networks mimics the cellular state transition (or mode switch) in a living cell in response to a specific number of prerequisites. Two different signaling regulations, namely absence receptor regulation and presence receptor regulation, are represented in our network model. An evolutionary argument based on a minimum evolution hypothesis accounts for the empirical observation that an absence receptor-regulated network is more likely to regulate a mode switch than a presence receptor-regulated network. We simulated the evolution of networks regulated by absence and/or presence receptors. The only simulation that produced networks of maximum fitness had only absence receptors. We developed a model to calculate the probability of evolving a maximum-fitness, minimum-evolution network. The calculation gives a qualitative view of the complexity of signaling network evolution.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Biophysics and Computational Biology

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Zou, Lihua

Contributors dc:contributor

• Mittenthal, Jay E.

• Biophysics, General

Language dc:language

eng

Identifier

(MiAaPQ)AAI3131064

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/85438